User localization capabilities are of significant importance to modern information and communication networks. In designing and deploying their own infrastructures, network providers have addressed the need to provide user localization capabilities. In particular, the ability of network systems to automatically retrieve (or to retrieve in an on-demand fashion) exact positions of users enables providers of over-the-top applications (or even the network provider itself) to deliver additional (and advanced) services that rely on user context information. In addition, the ability of network systems to determine exact positions of users is also of significant benefit in emergency situations. Due to their growing ubiquity, cellular network deployments are more frequently being utilized in responding to such emergency situations.
User localization capabilities have been deeply discussed within the international standardization groups (ISG) in order to provide common guidelines for different network equipment producers to adhere to. The 3GPP standard body has enumerated several localization techniques. A number of localization techniques rely on data observed by network entities. For example, in Cell ID (CID) techniques, network systems localize cellular devices to the geographical position of its serving base station. However, given the often broad range of coverage of each single cell, CID techniques do not allow network systems to obtain exact positions of users. CID can be enhanced by exploiting ranging measurements such as Time of Arrival (TA) or Round-Trip Time (RTT) measurements. For example, in Enhanced Cell ID (E-CID) techniques, CID techniques are combined with additional measurements such as Angle of Arrival (AoA) measurements, timing (TA or RTT) measurements, and signal strength measurements. In Radio Frequency Pattern Matching (RFPM) techniques, network systems localize each respective cellular device at a geographical position associated with a signal fingerprint (selected from an available fingerprint database) that best matches the respective cellular device. The fingerprints in such fingerprint database are typically made by Received Signal Strength (RSS) measurements. An example of such an RFPM technique is provided in the non-patent literature of “RF pattern matching location method in LTE, release 12, V12.0.0,” 3GPP, Sophia Antipolis, France, Rep. 3GPP TR 36.809, September 2013.
Additional localization techniques are based on trilateration processes. For example, in Uplink Time Difference of Arrival (UTDoA), network systems localize cellular devices by comparing a time difference of arrival of uplink signals, such as a Sounding Reference Signal (SRS), to Location Measurement Units (LMUs) placed at known locations. An example of such an UTDoA technique is provided in the non-patent literature of “LMU performance specification; network based positioning systems in E-UTRAN, release 11, V11.4.0,” 3GPP, Sophia Antipolis, France, Rep. 3GPP TS 36.111, October 2014. In Observed Time Difference of Arrival (OTDoA) techniques, a user device obtains measurements of a time difference between Positioning Reference Signals (PRSs) received from different eNodeBs and reports back such measurements to a Location Server (LS). The LS then localizes the device by using the measurements reported by the device. An example of such an OTDoA technique is provided in the non-patent literature of “Stage 2 functional specification of UE positioning in E-UTRAN, release 14, V14.0.0,” 3GPP, Sophia Antipolis, France, Rep. 3GPP TS 36.305, December 2016. Both UTDoA and OTDoA require coordination between different eNBs to apply the trilateration concept. This can result in additional delays and decreased accuracy due to increased randomness of wireless channel conditions resulting from an increase in the number of paths between the user and the eNBs.
In addition to the aforementioned 3GPP localization techniques, a number of additional non-3GPP localization techniques have also previously been employed. For example, in assisted GNSS (A-GNSS), assistance data of GNSS systems is distributed in a cellular network to aid a GNSS receiver of the cellular device. In barometric techniques, on-board barometric sensors are utilized to bring improvements in vertical accuracy of the localization. However, such non-3GPP techniques require non-3GPP equipment can require the use of untrusted information.
Furthermore, several localization techniques employ the use of unmanned aerial vehicles (UAVs). A localization algorithm for use in a Wireless Sensor Network (WSN) that employs a UAV equipped with directional antennas is described in the non-patent literature of Sorbelli, S. K. Das, C. M. Pinotti and S. Silvestri, “Precise Localization in Sparse Sensor Networks Using a Drone with Directional Antennas”, ACM ICDCN '18, January 2018. In the localization algorithm described therein, each sensor is aware of a path of a drone and can calculate its own positioned using a trilateration process.
A UAV-based localization system that makes use of a combination of RSSI and AOA techniques to localize users is described in the non-patent literature of A. Wang, X. Ji, D. Wu, et al., “GuideLoc: UAV-Assisted Multitarget Localization System for Disaster Rescue,” Mobile Information Systems, 2017. In the localization system described therein, a preliminary partitioning of a target area is performed if it is larger than or equal to a communication range of the wireless devices. Thereafter, a UAV flies along an optimal path throughout all the unit partitions. When the UAV arrives at a unit partition center, it visits the current unit partition only if one or more targets are in range, otherwise it moves to the next one. If targets are detected, the UAV estimates their location by means of relative received signal strength (RSSI) and AOA, and uses a genetic algorithm to plan an optimal path to visit each target. The AOA technique is used to estimate a direction in which the UAV must fly in order to reach a target. During the flight, an averaging method on RSSI is used to recognize whether the UAV is over the target. In the affirmative case, GuideLoc infers the target's coordinates as the GPS coordinates of the UAV.
A localization technique utilizing a high altitude platforms (HAPs) for determining geographical positions of ground users is described in U.S. Pat. No. 9,571,978. However, HAPs are usually placed at very high altitudes that can cause inaccuracies in a user position discovery process. In addition, HAPs are not easily moved, and therefore, are not able to easily follow a pre-defined trajectory in order to speed up a user localization process.